Nowadays, clinical medical imaging plays an important role to provide doctors with the necessary information about patients. For example, a variety of imaging modalities, such as CT, MRI, Ultrasound or the like, are currently available to assist doctors in this regard. For each imaging modality, there are different imaging modes.
Taking Ultrasound as an example, ultrasound imaging has been widely applied in clinical applications due to it being a non-radiation, non-invasive, real-time and low-cost technique. As is well known in the art, there are different kinds of modes in ultrasound imaging, for example but not limited to, B-mode ultrasound, Color ultrasound, Contrast ultrasound, Elastography ultrasound including Strain ultrasound and Quantitative Elastography ultrasound.
In order to have comprehensive information about patients, doctors often need to combine imaging data from different imaging modes. How to optimally use all the imaging data is a difficult problem for human beings. The main reason is that the clinical object exists in a high dimensional space and human perception is limited and lacks the competence to solve the high-dimensional problem.
Computerized techniques such as machine learning are better capable to handle the high dimensional problem than human beings. Therefore, a clinical decision support (CDS) system based on computerized techniques plays an important role in providing such comprehensive information for doctors.
However, for a clinical object, doctors are required to first select the region of interest (ROI) for denoting the object in an imaging plane or imaging volume of a patient and then apply the related analysis or computerized algorithms to provide the structural information, functional information or even the diagnostic information themselves.
U.S. Pat. No. 6,186,949 B1 discloses method and apparatus for three-dimensional flow imaging using coded excitation. In performing three-dimensional flow imaging using coded excitation and wall filtering, a coded sequence of broadband pulses (centered at a fundamental frequency) is transmitted multiple times to a particular transmit focal position. On receive, the receive signals acquired for each firing are compressed and bandpass filtered to isolate a compressed pulse centered at the fundamental frequency. The compressed and isolated signals are then wall filtered to extract the flow imaging data. This process is repeated for a multiplicity of transmit focal positions in each of a multiplicity of scanning planes to acquire a volume of flow imaging data. Volume rendered images are then produced which allow the user to view the data volume from any angle. In addition, the data volume may be reformatted to produce two-dimensional images of arbitrary cut planes through the data volume.